Time-series based analytics using video streams

ABSTRACT

Methods and systems for detecting and predicting anomalies include processing frames of a video stream to determine values of a feature corresponding to each frame. A feature time series is generated that corresponds to values of the identified feature over time. A matrix profile is generated that identifies similarities of sub-sequences of the time series to other sub-sequences of the feature time series. An anomaly is detected by determining that a value of the matrix profile exceeds a threshold value. An automatic action is performed responsive to the detected anomaly.

RELATED APPLICATION INFORMATION

This application claims priority to U.S. Patent Application No.63/006,246, filed on Apr. 7, 2020, incorporated herein by reference inits entirety.

BACKGROUND Technical Field

The present invention relates to video analysis, and, more particularly,to the use of video streams to generate time series data that can beused with time series analytics.

Description of the Related Art

Deep learning-based computer vision technologies are becomingincreasingly common, with video cameras being deployed widely and inmany different contexts. This provides a wealth of information aboutuser activities. However, a number of existing types of analytics cannotbe used directly on video stream data.

SUMMARY

A method for detecting and responding to an anomaly includes processingframes of a video stream to determine values of a feature correspondingto each frame. A feature time series is generated that corresponds tovalues of the identified feature over time. A matrix profile isgenerated that identifies similarities of sub-sequences of the timeseries to other sub-sequences of the feature time series. An anomaly isdetected by determining that a value of the matrix profile exceeds athreshold value. An automatic action is performed responsive to thedetected anomaly.

A method for predicting an anomaly includes generating a time series,based on frames of a video stream. A matrix profile is determined, basedon the time series, that identifies similarities of sub-sequences of thetime series to other sub-sequences of the time series. An anomaly isdetected by determining that a value of the matrix profile exceeds athreshold value. An anomaly precursor is identified that includes apattern from the time series occurring before the detected anomaly. Theanomaly precursor is detected in a newly received sub-sequence of thetime series to predict an anomaly, by matching the identified anomalyprecursor to the newly received sub-sequence. An automatic action isperformed to prevent the predicted anomaly.

A system for detecting and responding to an anomaly includes a hardwareprocessor and a memory that stores a computer program product. When thecomputer program product is executed by a computer, it causes thehardware processor to process frames of a video stream to determinevalues of a feature corresponding to each frame, to generate a featuretime series that corresponds to values of the identified feature overtime, to determine a matrix profile that identifies similarities ofsub-sequences of the time series to other sub-sequences of the featuretime series, to detect an anomaly by determining that a value of thematrix profile exceeds a threshold value, and to perform an automaticaction responsive to the detected anomaly.

These and other features and advantages will become apparent from thefollowing detailed description of illustrative embodiments thereof,which is to be read in connection with the accompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

The disclosure will provide details in the following description ofpreferred embodiments with reference to the following figures wherein:

FIG. 1 is a diagram of an environment that is monitored by various videostreams, where anomalous behavior may be detected from time seriesinformation generated by the time series, in accordance with anembodiment of the present invention;

FIG. 2 is a block/flow diagram of a method of detecting and respondingto an anomaly in video time series information, in accordance with anembodiment of the present invention;

FIG. 3 is a block/flow diagram showing detail on the generation of atime series and on the creation of a matrix profile, in accordance withan embodiment of the present invention;

FIG. 4 is a diagram illustrating a time series and a correspondingmatrix profile, in accordance with an embodiment of the presentinvention;

FIG. 5 is a block/flow diagram of a method of predicting and preventingan anomaly in a video time series information, in accordance with anembodiment of the present invention; and

FIG. 6 is a block diagram of an anomaly prevention and response systemthat detects, predicts, prevents, and responds to anomalies in timeseries information, in accordance with an embodiment of the presentinvention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Video stream information may be converted to time series data, where ameasurement is extracted from the successive images of the video streamover a period of time. This time series data can then be analyzed usinga matrix profile for the performance of an analysis. The matrix profilehelps to identify periods of normal behavior and periods of anomalousbehavior, which a system can then use to automatically respond tochanging circumstances.

Referring now in detail to the figures in which like numerals representthe same or similar elements and initially to FIG. 1 , an environment100 is shown. For example, one type of environment that is contemplatedis a mall or shopping center, which may include a common space 102 andone or more regions 104, such as a store. It should be understood thatthis example is provided solely for the purpose of illustration, andshould not be regarded as limiting.

A boundary is shown between the common space 102 and the region 104. Theboundary can be any appropriate physical or virtual boundary. Examplesof physical boundaries include walls and rope—anything that establishesa physical barrier to passage from one region to the other. Examples ofvirtual boundaries include a painted line and a designation within a mapof the environment 100. Virtual boundaries do not establish a physicalbarrier to movement, but can nonetheless be used to identify regionswithin the environment. For example, a region of interest may beestablished next to an exhibit or display, and can be used to indicatepeople's interest in that display. A gate 106 is shown as a passagewaythrough the boundary, where individuals are permitted to pass betweenthe common space 102 and the region 104.

The environment 100 is monitored by a number of video cameras 114.Although this embodiment shows the cameras 114 being positioned at thegate 106 and along a border between regions, it should be understoodthat such cameras can be positioned anywhere within the common space 102and the region 104. The video cameras 114 capture live streaming videoof the individuals in the environment. A number of individuals areshown, including untracked individuals 108, shown as triangles, andtracked individuals 110, shown as circles. Also shown is a trackedperson of interest 112, shown as a square. In some examples, all of theindividuals may be tracked individuals.

In addition to capturing visual information, the cameras 114 may captureother types of data. For example, the cameras 114 may be equipped withinfrared sensors that can read the body temperature of an individual. Inassociation with the visual information, this can provide the ability toremotely identify individuals who are sick, and to track their motionthrough the environment.

As a tracked individual 110 moves through the environment 100, they maymove out of the visual field of one video camera 114 and into the visualfield of another video camera. The tracked individual 110 mayfurthermore enter a region that is not covered by the visual field ofany of the video cameras 114. Additionally, as the tracked individual110 moves, a camera's view of their face may become obstructed byclothing, objects, or other people. The different images of the trackedindividual's face, across time and space, associated together to linksvideos of individuals in different places and at different times.

During operation, each of the cameras 114 may generate a respectivevideo stream. These video streams may be processed, and the frames ofthe respective video streams may be analyzed. For example, each framemay be processed to identify people, faces, vehicles, or any other formof object. Such processing may include object localization, where abounding box may be established around a detected object. The videostreams may also indicate particular events, such as the detection ofmotion, a change in an environmental condition such as lights beingturned on, or the occurrence of particular temperature conditions usinginfrared cameras. Each frame may be timestamped, so that the time ofeach occurrence, event, or detection may be identified and tracked.

In one example, a particular camera 114 may overlook a specific gate106, where people continually pass through. The video stream that isgenerated by the camera 114 may be processed to determine how manypeople are detected in each frame. In this manner, the number of peoplepassing through the gate 106 may be determined as a function of time.Additional processing may be performed, for example by trackinginformation from frame to frame. An example of such additionalprocessing may be to keep track of faces or other identifyinginformation as people pass by, making it possible to determine a numberof unique individuals who have passed through the gate 106.

Referring now to FIG. 2 , a method of detecting and responding toanomalies in video streams is shown. Block 202 records a video streamfrom a camera 114. This video stream may include a series of individualimage frames, and may further include additional information, such asmetadata and sensor measurements. Each frame may be associated with arespective timestamp, indicating a time at which the image was capturedand providing an order between the frames of the video stream.

Block 204 processes the individual frames of the video stream. Thisprocessing may include, for example, detecting objects within theframes, identifying bounding boxes of detected objects, inferringinteractions between objects in the frames based on relative positions,detecting the occurrence of an event, correlating multiple events, etc.The processing may provide a set of features that are associated witheach frame, such as with a binary-valued, continuously-valued, ormixed-valued feature vector, with each element of the feature vectorrepresenting the occurrence of a respective event. For example, theprocessing may determine whether a person is present in a frame, howmany people are detected within the frame, a maximum body temperature ofthe people detected in the frame, whether a person is within aparticular region of interest in the frame, or any other feature orcombination of features.

Block 206 generates a time series, based on the feature(s) detected inthe individual frames of the video stream. For example, one time seriesmay track times at which a person has been detected. Another time seriesmay include a time series may track how many people are detected withinthe video stream over time. Because each frame may have a respectivetime stamp, these occurrences may be ordered in time. Multiple timeseries may be generated for each video stream in this manner,representing different types of processing that were performed.

Using the time series, block 208 creates a matrix profile. A matrixprofile may annotate the time series to identify patterns. In one sense,a matrix profile may represent a distance matrix of all pairs ofsubsequences of length m within the time series. The different values ofthe matrix may then represent the similarity of a given subsequence tothe other subsequences in the time series, with a smallest non-diagonalvalue indicating whether a given subsequence has occurred before. Anyappropriate similarity metric may be used to generate a value thatreflects the similarity of two time series sub-sequences. Thus, thematrix profile may generate relatively flow values at times when thetime series is acting in a manner that reoccurs, and have relativelyhigh values during anomalous times.

Using the matrix profile, block 210 identifies anomalies, for example byidentifying timestamps at which the matrix profile exceeds a thresholdvalue. Such high values represent subsequences of the time series thathave a relatively unique shape, indicating behavior that is out of thenorm. For example, if a given video stream includes frames of peopleentering a door during particular times of the day, anomalous behaviormight include a person entering the door at an unusual time of day. Thematrix profile thereby captures contextual information, beyond just thesingular occurrence of an event within a video frame, to distinctlyidentify anomalous behavior.

Block 212 then responds to the identified anomaly. This response mayinclude referring the matter to a human operator, such as by alertingsecurity personnel. The response may include performing an automaticaction, such as locking doors, sounding an alarm, changing theoperational status of one or more machines, triggering an environmentalcontrol, triggering a fire suppression system, etc.

Referring now to FIG. 3 , additional detail is provided on blocks 206and 208. Although the process of FIG. 2 is shown as being linear, itshould be understood that certain elements may be performedcontinuously. For example, block 202 may continuously record videostream and block 204 may continuously process incoming frames toidentify events. The process of generating the time series in block 206and creating the matrix profile in block 208 may therefore be performediteratively, as new information comes in.

As block 204 processes the new frames of video, block 302 receives thesenew frames and their associated feature information. Block 304 then addsthe new frame's information to the time series. If this is a new timeseries, then block 304 may create the first datapoint of the timeseries. Otherwise, the new frame may be appended to an existing timeseries. In the event that a frame is received out of order, block 304may insert the new frame into the correct position in the time series,based on its timestamp.

Block 306 then generates a new sub-sequence, based on the updatedtime-series. This new sub-sequence may include a set of m frames, or mayspan a number of frames that occur within a timeframe m. Block 308 maythen update the matrix profile to include the new sub-sequence, with anew value being generated that represents the similarity of the latestsub-sequence to other sub-sequences of the time series.

Referring now to FIG. 4 , a comparison between a time series 410 and acorresponding matrix profile 420 is shown. In each graph, the horizontalaxis represents time. For the time series 410, the vertical axisrepresents a number of object detections within a frame, such as a countof a number of people. For the matrix profile 420, the vertical axisrepresents the degree of similarity between a corresponding sub-sequenceand other sub-sequences in the time-series.

A period of time 402 is indicated, corresponding to relatively highvalues of the matrix profile 420. These values correspond to a clearchange in the behavior of the time series 410, where the amplitude andwaveform shape changes, before settling into a new pattern. The matrixprofile provides a way to automatically identify such patterns, and todo so over a much larger time scale than would be practical for a humanbeing.

Identifying the time in which an anomaly occurs can help withidentifying the root cause of a problem. For example, if a given anomalyoccurs close in time to some known problem, then the corresponding videomay be reviewed to identify changes in the recorded behavior that mightexplain the problem.

Additionally, this information may be used to predict anomalies, byidentifying precursor behavior for anomalies that have been seen before.

Referring now to FIG. 5 , a method of anomaly prediction is shown, usinganomaly precursor detection. Block 502 detects an anomaly, for exampleusing the matrix profile described above. Block 504 then identifies ananomaly precursor. The anomaly precursor may include time seriesinformation that comes shortly before the anomaly is detected. Theanomaly precursor may not itself rise to the level of being an anomaly,but may be associated with an anomaly that is about to occur. Forexample, as the amount of foot traffic through a region of interest isdetected as being anomalous, there may be a period of increase that isbelow the threshold. Block 504 identifies this precursor, for example asa pattern in the time series. Block 506 saves the precursor pattern.

As the video stream continues to be monitored, the precursor pattern maybe matched against new sub-sequences of the time series. This matchingmay include a similarity matching, where an above-threshold similaritybetween a saved precursor pattern and a time series sub-sequenceindicates that an anomaly precursor has been detected in block 510. Anyappropriate similarity metric may be used to determine a value thatreflects the similarity between a precursor pattern and a time seriessub-sequence. A preventative action may then be performed in block 512.The preventative action may be any appropriate action that can be usedto prevent the anomaly, or to minimize its impact. For example, thepreventative action may include diverting traffic, engaging ordisengaging traffic control devices, sending security personnel to aregion of interest, etc. The preventative action may include, forexample, any action described above as a responsive action.

Embodiments described herein may be entirely hardware, entirely softwareor including both hardware and software elements. In a preferredembodiment, the present invention is implemented in software, whichincludes but is not limited to firmware, resident software, microcode,etc.

Embodiments may include a computer program product accessible from acomputer-usable or computer-readable medium providing program code foruse by or in connection with a computer or any instruction executionsystem. A computer-usable or computer readable medium may include anyapparatus that stores, communicates, propagates, or transports theprogram for use by or in connection with the instruction executionsystem, apparatus, or device. The medium can be magnetic, optical,electronic, electromagnetic, infrared, or semiconductor system (orapparatus or device) or a propagation medium. The medium may include acomputer-readable storage medium such as a semiconductor or solid statememory, magnetic tape, a removable computer diskette, a random accessmemory (RAM), a read-only memory (ROM), a rigid magnetic disk and anoptical disk, etc.

Each computer program may be tangibly stored in a machine-readablestorage media or device (e.g., program memory or magnetic disk) readableby a general or special purpose programmable computer, for configuringand controlling operation of a computer when the storage media or deviceis read by the computer to perform the procedures described herein. Theinventive system may also be considered to be embodied in acomputer-readable storage medium, configured with a computer program,where the storage medium so configured causes a computer to operate in aspecific and predefined manner to perform the functions describedherein.

A data processing system suitable for storing and/or executing programcode may include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code to reduce the number of times code is retrieved frombulk storage during execution. Input/output or I/O devices (includingbut not limited to keyboards, displays, pointing devices, etc.) may becoupled to the system either directly or through intervening I/Ocontrollers.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modem and Ethernet cards are just a few of thecurrently available types of network adapters.

As employed herein, the term “hardware processor subsystem” or “hardwareprocessor” can refer to a processor, memory, software or combinationsthereof that cooperate to perform one or more specific tasks. In usefulembodiments, the hardware processor subsystem can include one or moredata processing elements (e.g., logic circuits, processing circuits,instruction execution devices, etc.). The one or more data processingelements can be included in a central processing unit, a graphicsprocessing unit, and/or a separate processor- or computing element-basedcontroller (e.g., logic gates, etc.). The hardware processor subsystemcan include one or more on-board memories (e.g., caches, dedicatedmemory arrays, read only memory, etc.). In some embodiments, thehardware processor subsystem can include one or more memories that canbe on or off board or that can be dedicated for use by the hardwareprocessor subsystem (e.g., ROM, RAM, basic input/output system (BIOS),etc.).

In some embodiments, the hardware processor subsystem can include andexecute one or more software elements. The one or more software elementscan include an operating system and/or one or more applications and/orspecific code to achieve a specified result.

In other embodiments, the hardware processor subsystem can includededicated, specialized circuitry that performs one or more electronicprocessing functions to achieve a specified result. Such circuitry caninclude one or more application-specific integrated circuits (ASICs),field-programmable gate arrays (FPGAs), and/or programmable logic arrays(PLAs).

These and other variations of a hardware processor subsystem are alsocontemplated in accordance with embodiments of the present invention.

Referring now to FIG. 6 , an anomaly prediction and response system 600is shown. The system 600 includes a hardware processor 602 and a memory604. The system 600 may also include a number of functional modules thatmay be implemented as software, which maybe stored in the memory 604 andwhich may be executed by the hardware processor 602 to perform therespective function(s).

A camera interface 606 receives a video stream from one or more cameras114. The camera interface 606 may be a dedicated analog or digitalinterface that received information directly from the cameras 114, ormay be a network interface that receives video streams in the form ofnetwork data. The camera interface 606 may communicate with the cameras114 by any appropriate wired or wireless communications medium andprotocol.

A frame processor 608 processes the frames of the received videostream(s), for example by performing object detection or any otherappropriate form of analysis. Time series generator 610 then generates atime series from the frames, representing detected features along withtheir respective times of detection. A matrix profile generator 612converts the time series into a matrix profile, with a value that variesin accordance with how similar a given sub-sequence of the time seriesis to other sub-sequences.

Anomaly detector 614 uses the matrix profile to identify periods ofanomalous activity. This triggers an automatic responsive action 618,which seeks to address the anomaly. After an anomaly has been detected,a precursor detector 616 may identify a corresponding precursorsub-sequence from the time series. As new video frames arrive and areadded to the time series by time series generator 610, the precursordetector 616 identifies new sub-sequences that are similar to apreviously identified precursor sub-sequence. The precursor detector 616then triggers an automatic preventative action 620.

Reference in the specification to “one embodiment” or “an embodiment” ofthe present invention, as well as other variations thereof, means that aparticular feature, structure, characteristic, and so forth described inconnection with the embodiment is included in at least one embodiment ofthe present invention. Thus, the appearances of the phrase “in oneembodiment” or “in an embodiment”, as well any other variations,appearing in various places throughout the specification are notnecessarily all referring to the same embodiment. However, it is to beappreciated that features of one or more embodiments can be combinedgiven the teachings of the present invention provided herein.

It is to be appreciated that the use of any of the following “/”,“and/or”, and “at least one of”, for example, in the cases of “A/B”, “Aand/or B” and “at least one of A and B”, is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of both options (A andB). As a further example, in the cases of “A, B, and/or C” and “at leastone of A, B, and C”, such phrasing is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of the third listedoption (C) only, or the selection of the first and the second listedoptions (A and B) only, or the selection of the first and third listedoptions (A and C) only, or the selection of the second and third listedoptions (B and C) only, or the selection of all three options (A and Band C). This may be extended for as many items listed.

The foregoing is to be understood as being in every respect illustrativeand exemplary, but not restrictive, and the scope of the inventiondisclosed herein is not to be determined from the Detailed Description,but rather from the claims as interpreted according to the full breadthpermitted by the patent laws. It is to be understood that theembodiments shown and described herein are only illustrative of thepresent invention and that those skilled in the art may implementvarious modifications without departing from the scope and spirit of theinvention. Those skilled in the art could implement various otherfeature combinations without departing from the scope and spirit of theinvention. Having thus described aspects of the invention, with thedetails and particularity required by the patent laws, what is claimedand desired protected by Letters Patent is set forth in the appendedclaims.

What is claimed is:
 1. A method of detecting and responding to ananomaly, comprising: processing frames of a video stream to determinevalues of a feature corresponding to each frame; generating a featuretime series that corresponds to values of the identified feature overtime; determining a matrix profile that identifies similarities ofsub-sequences of the time series to other sub-sequences of the featuretime series, wherein the matrix profile includes a matrix havingelements that each represent a similarity between a respective pair ofsub-sequences of the time series and further includes a profile timeseries having values that correspond to each sub-sequence of the featuretime series and that are determined by the most similar othersub-sequence of the feature time series; detecting an anomaly bydetermining that a value of the matrix profile exceeds a thresholdvalue; and performing an automatic action responsive to the detectedanomaly.
 2. The method of claim 1, wherein processing the frames of thevideo stream includes performing object detection.
 3. The method ofclaim 2, wherein the feature time series includes a number of detectedobjects at each frame.
 4. The method of claim 1, updating the featuretime series and the matrix profile in response to newly received framesof the video stream.
 5. The method of claim 1, wherein the automaticaction includes an action selected from the group consisting of soundingan alarm, locking doors, engaging or disengaging traffic controldevices, changing the operational status of one or more machines,triggering an environmental control, and triggering a fire suppressionsystem.
 6. A method of predicting an anomaly, comprising: generating atime series, based on frames of a video stream; determining a matrixprofile, based on the time series, that identifies similarities ofsub-sequences of the time series to other sub-sequences of the timeseries, wherein the matrix profile includes a matrix having elementsthat each represent a similarity between a respective pair ofsub-sequences of the time series and further includes a profile timeseries having values that correspond to each sub-sequence of the featuretime series and that are determined by the most similar othersub-sequence of the feature time series; detecting an anomaly bydetermining that a value of the matrix profile exceeds a thresholdvalue; identifying an anomaly precursor that includes a pattern from thetime series occurring before the detected anomaly; detecting the anomalyprecursor in a newly received sub-sequence of the time series to predictan anomaly, by matching the identified anomaly precursor to the newlyreceived sub-sequence; and performing an automatic action to prevent thepredicted anomaly.
 7. The method of claim 6, wherein the automaticaction includes an action selected from the group consisting of soundingan alarm, locking doors, engaging or disengaging traffic controldevices, changing the operational status of one or more machines,triggering an environmental control, and triggering a fire suppressionsystem.
 8. The method of claim 6, further comprising saving theidentified anomaly in a precursor database.
 9. The method of claim 8,further comprising generating the newly generated sub-sequence from newtime series information, wherein matching the identified anomalyprecursor to the newly received sub-sequence includes determining asimilarity between the newly generated sub-sequence to each identifiedanomaly in the precursor database.
 10. A system for detecting andresponding to an anomaly, comprising: a hardware processor; a memorythat stores a computer program product, which, when executed by thehardware processor, causes the hardware processor to: process frames ofa video stream to determine values of a feature corresponding to eachframe; generate a feature time series that corresponds to values of theidentified feature over time; determine a matrix profile that identifiessimilarities of sub-sequences of the time series to other sub-sequencesof the feature time series, wherein the matrix profile includes a matrixhaving elements that each represent a similarity between a respectivepair of sub-sequences of the time series and further includes a profiletime series having values that correspond to each sub-sequence of thefeature time series and that are determined by the most similar othersub-sequence of the feature time series; detect an anomaly bydetermining that a value of the matrix profile exceeds a thresholdvalue; and perform an automatic action responsive to the detectedanomaly.
 11. The system of claim 10, wherein the computer programproduct further causes the hardware processor to perform objectdetection.
 12. The system of claim 11, wherein the feature time seriesincludes a number of detected objects at each frame.
 13. The system ofclaim 10, wherein the computer program product further causes thehardware processor to update the feature time series and the matrixprofile in response to newly received frames of the video stream. 14.The system of claim 10, wherein the automatic action includes an actionselected from the group consisting of sounding an alarm, locking doors,engaging or disengaging traffic control devices, changing theoperational status of one or more machines, triggering an environmentalcontrol, and triggering a fire suppression system.